Evaluating AI agents in production is a significant challenge, as traditional software testing methods fall short. This article presents a comprehensive architecture for agent evaluation, addressing the core question: 'Is this agent actually helping the business?' The system is built on metric hierarchies that categorize evaluation criteria, ground truth (GT) sources that provide reliable baselines, and judge calibration to ensure consistent scoring. This creates a regression-quality closed loop that can be automated and integrated into CI/CD pipelines. The technical depth is high, covering practical implementation details for engineering teams. The approach is evergreen as agent-based systems become more prevalent in enterprise applications. The commercial value is significant for organizations deploying AI agents at scale, as it provides a framework for quality assurance and continuous improvement.
A detailed architecture for evaluating AI agents in production, focusing on metric hierarchies, ground truth sources, and judge calibration.